Structure updated and intqpipopt files added

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diff --git a/newstructure/thirdparty/linux/include/coin/ClpSimplexPrimal.hpp b/newstructure/thirdparty/linux/include/coin/ClpSimplexPrimal.hpp
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+/* $Id: ClpSimplexPrimal.hpp 1665 2011-01-04 17:55:54Z lou $ */
+// Copyright (C) 2002, International Business Machines
+// Corporation and others.  All Rights Reserved.
+// This code is licensed under the terms of the Eclipse Public License (EPL).
+/*
+   Authors
+
+   John Forrest
+
+ */
+#ifndef ClpSimplexPrimal_H
+#define ClpSimplexPrimal_H
+
+#include "ClpSimplex.hpp"
+
+/** This solves LPs using the primal simplex method
+
+    It inherits from ClpSimplex.  It has no data of its own and
+    is never created - only cast from a ClpSimplex object at algorithm time.
+
+*/
+
+class ClpSimplexPrimal : public ClpSimplex {
+
+public:
+
+     /**@name Description of algorithm */
+     //@{
+     /** Primal algorithm
+
+         Method
+
+        It tries to be a single phase approach with a weight of 1.0 being
+        given to getting optimal and a weight of infeasibilityCost_ being
+        given to getting primal feasible.  In this version I have tried to
+        be clever in a stupid way.  The idea of fake bounds in dual
+        seems to work so the primal analogue would be that of getting
+        bounds on reduced costs (by a presolve approach) and using
+        these for being above or below feasible region.  I decided to waste
+        memory and keep these explicitly.  This allows for non-linear
+        costs!  I have not tested non-linear costs but will be glad
+        to do something if a reasonable example is provided.
+
+        The code is designed to take advantage of sparsity so arrays are
+        seldom zeroed out from scratch or gone over in their entirety.
+        The only exception is a full scan to find incoming variable for
+        Dantzig row choice.  For steepest edge we keep an updated list
+        of dual infeasibilities (actually squares).
+        On easy problems we don't need full scan - just
+        pick first reasonable.  This method has not been coded.
+
+        One problem is how to tackle degeneracy and accuracy.  At present
+        I am using the modification of costs which I put in OSL and which was
+        extended by Gill et al.  I am still not sure whether we will also
+        need explicit perturbation.
+
+        The flow of primal is three while loops as follows:
+
+        while (not finished) {
+
+          while (not clean solution) {
+
+             Factorize and/or clean up solution by changing bounds so
+         primal feasible.  If looks finished check fake primal bounds.
+         Repeat until status is iterating (-1) or finished (0,1,2)
+
+          }
+
+          while (status==-1) {
+
+            Iterate until no pivot in or out or time to re-factorize.
+
+            Flow is:
+
+            choose pivot column (incoming variable).  if none then
+        we are primal feasible so looks as if done but we need to
+        break and check bounds etc.
+
+        Get pivot column in tableau
+
+            Choose outgoing row.  If we don't find one then we look
+        primal unbounded so break and check bounds etc.  (Also the
+        pivot tolerance is larger after any iterations so that may be
+        reason)
+
+            If we do find outgoing row, we may have to adjust costs to
+        keep going forwards (anti-degeneracy).  Check pivot will be stable
+        and if unstable throw away iteration and break to re-factorize.
+        If minor error re-factorize after iteration.
+
+        Update everything (this may involve changing bounds on
+        variables to stay primal feasible.
+
+          }
+
+        }
+
+        TODO's (or maybe not)
+
+        At present we never check we are going forwards.  I overdid that in
+        OSL so will try and make a last resort.
+
+        Needs partial scan pivot in option.
+
+        May need other anti-degeneracy measures, especially if we try and use
+        loose tolerances as a way to solve in fewer iterations.
+
+        I like idea of dynamic scaling.  This gives opportunity to decouple
+        different implications of scaling for accuracy, iteration count and
+        feasibility tolerance.
+
+        for use of exotic parameter startFinishoptions see Clpsimplex.hpp
+     */
+
+     int primal(int ifValuesPass = 0, int startFinishOptions = 0);
+     //@}
+
+     /**@name For advanced users */
+     //@{
+     /// Do not change infeasibility cost and always say optimal
+     void alwaysOptimal(bool onOff);
+     bool alwaysOptimal() const;
+     /** Normally outgoing variables can go out to slightly negative
+         values (but within tolerance) - this is to help stability and
+         and degeneracy.  This can be switched off
+     */
+     void exactOutgoing(bool onOff);
+     bool exactOutgoing() const;
+     //@}
+
+     /**@name Functions used in primal */
+     //@{
+     /** This has the flow between re-factorizations
+
+         Returns a code to say where decision to exit was made
+         Problem status set to:
+
+         -2 re-factorize
+         -4 Looks optimal/infeasible
+         -5 Looks unbounded
+         +3 max iterations
+
+         valuesOption has original value of valuesPass
+      */
+     int whileIterating(int valuesOption);
+
+     /** Do last half of an iteration.  This is split out so people can
+         force incoming variable.  If solveType_ is 2 then this may
+         re-factorize while normally it would exit to re-factorize.
+         Return codes
+         Reasons to come out (normal mode/user mode):
+         -1 normal
+         -2 factorize now - good iteration/ NA
+         -3 slight inaccuracy - refactorize - iteration done/ same but factor done
+         -4 inaccuracy - refactorize - no iteration/ NA
+         -5 something flagged - go round again/ pivot not possible
+         +2 looks unbounded
+         +3 max iterations (iteration done)
+
+         With solveType_ ==2 this should
+         Pivot in a variable and choose an outgoing one.  Assumes primal
+         feasible - will not go through a bound.  Returns step length in theta
+         Returns ray in ray_
+     */
+     int pivotResult(int ifValuesPass = 0);
+
+
+     /** The primals are updated by the given array.
+         Returns number of infeasibilities.
+         After rowArray will have cost changes for use next iteration
+     */
+     int updatePrimalsInPrimal(CoinIndexedVector * rowArray,
+                               double theta,
+                               double & objectiveChange,
+                               int valuesPass);
+     /**
+         Row array has pivot column
+         This chooses pivot row.
+         Rhs array is used for distance to next bound (for speed)
+         For speed, we may need to go to a bucket approach when many
+         variables go through bounds
+         If valuesPass non-zero then compute dj for direction
+     */
+     void primalRow(CoinIndexedVector * rowArray,
+                    CoinIndexedVector * rhsArray,
+                    CoinIndexedVector * spareArray,
+                    int valuesPass);
+     /**
+         Chooses primal pivot column
+         updateArray has cost updates (also use pivotRow_ from last iteration)
+         Would be faster with separate region to scan
+         and will have this (with square of infeasibility) when steepest
+         For easy problems we can just choose one of the first columns we look at
+     */
+     void primalColumn(CoinIndexedVector * updateArray,
+                       CoinIndexedVector * spareRow1,
+                       CoinIndexedVector * spareRow2,
+                       CoinIndexedVector * spareColumn1,
+                       CoinIndexedVector * spareColumn2);
+
+     /** Checks if tentative optimal actually means unbounded in primal
+         Returns -3 if not, 2 if is unbounded */
+     int checkUnbounded(CoinIndexedVector * ray, CoinIndexedVector * spare,
+                        double changeCost);
+     /**  Refactorizes if necessary
+          Checks if finished.  Updates status.
+          lastCleaned refers to iteration at which some objective/feasibility
+          cleaning too place.
+
+          type - 0 initial so set up save arrays etc
+               - 1 normal -if good update save
+           - 2 restoring from saved
+          saveModel is normally NULL but may not be if doing Sprint
+     */
+     void statusOfProblemInPrimal(int & lastCleaned, int type,
+                                  ClpSimplexProgress * progress,
+                                  bool doFactorization,
+                                  int ifValuesPass,
+                                  ClpSimplex * saveModel = NULL);
+     /// Perturbs problem (method depends on perturbation())
+     void perturb(int type);
+     /// Take off effect of perturbation and say whether to try dual
+     bool unPerturb();
+     /// Unflag all variables and return number unflagged
+     int unflag();
+     /** Get next superbasic -1 if none,
+         Normal type is 1
+         If type is 3 then initializes sorted list
+         if 2 uses list.
+     */
+     int nextSuperBasic(int superBasicType, CoinIndexedVector * columnArray);
+
+     /// Create primal ray
+     void primalRay(CoinIndexedVector * rowArray);
+     /// Clears all bits and clears rowArray[1] etc
+     void clearAll();
+
+     /// Sort of lexicographic resolve
+     int lexSolve();
+
+     //@}
+};
+#endif
+